Download R for Macroecology Lecture 1

R for Macroecology
Aarhus University, Spring 2011
Why use R
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Scripted
Flexible
Free
Many extensions available
Huge support community
Brody’s rule of computers
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Computers make hard things easy and easy things hard
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The more sophisticated you get, the more true this becomes
(e.g. Excel vs. R)
Be prepared to spend lots of time on stupid things, but
know that the hard things will get done fast
The schedule
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Introduction to R and programming
Functions and plotting
Model specification, tests, and selection
Spatial data in R, integration with GIS
Spatial structure in data
Simultaneous autoregressive models
Project introduction (1 week) and work (2 weeks)
Presentation of project results
Today
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The structure of R
Functions, objects and programming
Reading and writing data
What is R?
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R is a statistical programming language
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Scripts
Plotting
System commands
The scripting interface in R is not very pretty
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PC – Tinn-R
Apple – TextWrangler
Rstudio
All provide syntax highlighting (very useful!)
The structure of R
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Functions
Objects
Control elements
The structure of R
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Functions (what do you want to do?)
Objects (what do you want to do it to?)
Control elements (when/how often do you want to do
it?)
The structure of R
Object
Function
Object
The structure of R
Object
Object
Object
Function
Object
The structure of R
Object
Object
Function
Object
Options
Object
The structure of R
Object
Object
Object
Function
Options
Arguments
Object
Return
The structure of R
Object
Object
Function
Object
Options
Object
Controlled by control elements (for, while, if)
Calling a function
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Call: a function with a particular set of arguments
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function( argument, argument . . . )
x = function( argument, argument . . .)
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sqrt(16)
[1] 4
x = sqrt(16)
x
[1] 4
Calling a function
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Call: a function with a particular set of arguments
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function( argument, argument . . . )
x = function( argument, argument . . .)
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sqrt(16)
[1] 4
x = sqrt(16)
x
[1] 4
The function return
is not saved, just
printed to the screen
Calling a function
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Call: a function with a particular set of arguments
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function( argument, argument . . . )
x = function( argument, argument . . .)
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sqrt(16)
[1] 4
x = sqrt(16)
x
[1] 4
The function return
is saved to a new
object, “x”
Arguments to a function
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function( argument, argument . . .)
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Many functions will have default values for arguments
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If unspecified, the argument will take that value
To find these values and a list of all arguments, do:
?function.name
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If you are just looking for functions related to a word, I
would use google. But you can also:
??key.word
What is an object?
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What size is it?
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Vector (one-dimensional, including length = 1)
Matrix (two-dimensional)
Array (n-dimensional)
What does it hold?
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Numeric (0, 0.2, Inf, NA)
Logical (T, F)
Factor (“Male”, “Female”)
Character (“Bromus diandrus”, “Bromus carinatus”, “Bison bison”)
Mixtures
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Lists
Dataframes
class() is a function that tells you what type of object the
argument is
Creating a numeric object
a = 10
a
[1] 10
a <- 10
a
[1] 10
10 -> a
a
[1] 10
Creating a numeric object
a = 10
a
[1] 10
a <- 10
a
[1] 10
10 -> a
a
[1] 10
All of these are
assignments
Creating a numeric object
a = a + 1
a
[1] 11
b = a * a
b
[1] 121
x = sqrt(b)
x
[1] 11
Creating a numeric object (length >1)
a = c(4,2,5,10)
a
[1] 4 2 5 10
a = 1:4
a
[1] 1 2 3 4
a = seq(1,10)
a
[1] 1 2 3 4 5 6 7 8 9 10
Creating a numeric object (length >1)
a = c(4,2,5,10)
a
[1] 4 2 5 10
a = 1:4
a
[1] 1 2 3 4
Two arguments
passed to this
function!
a = seq(1,10)
a
[1] 1 2 3 4 5 6 7 8 9 10
Creating a numeric object (length >1)
a = c(4,2,5,10)
a
[1] 4 2 5 10
a = 1:4
a
[1] 1 2 3 4
This function
returns a vector
a = seq(1,10)
a
[1] 1 2 3 4 5 6 7 8 9 10
Creating a matrix object
A = matrix(data = 0, nrow = 6, ncol = 5)
A
[,1] [,2] [,3] [,4] [,5]
[1,]
0
0
0
0
0
[2,]
0
0
0
0
0
[3,]
0
0
0
0
0
[4,]
0
0
0
0
0
[5,]
0
0
0
0
0
[6,]
0
0
0
0
0
Creating a logical object
3 < 5
[1] TRUE
3 > 5
[1] FALSE
x = 5
x == 5
[1] TRUE
x != 5
[1] FALSE
Conditional operators
<
>
<=
>=
==
!=
%in% &
|
Creating a logical object
3 < 5
[1] TRUE
3 > 5
[1] FALSE
Very important to
remember this
difference!!!
x = 5
x == 5
[1] TRUE
x != 5
[1] FALSE
Conditional operators
<
>
<=
>=
==
!=
%in% &
|
Creating a logical object
x = 1:10
x < 5
[1] TRUE TRUE TRUE TRUE FALSE
[6] FALSE FALSE FALSE FALSE FALSE
x == 2
[1] FALSE TRUE FALSE FALSE FALSE
[6] FALSE FALSE FALSE FALSE FALSE
Conditional operators
<
>
<=
>=
==
!=
%in% &
|
Getting at values
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R uses [ ] to refer to elements of objects
For example:
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V[5] returns the 5th element of a vector called V
M[2,3] returns the element in the 2nd row, 3rd column of
matrix M
M[2,] returns all elements in the 2nd row of matrix M
The number inside the brackets is called an index
Getting at a value from a numeric
a = c(3,2,7,8)
a[3]
[1] 7
a[1:3]
[1] 3 2 7
a[seq(2,4)]
[1] 2 7 8
Getting at a value from a numeric
a = c(3,2,7,8)
a[3]
[1] 7
a[1:3]
[1] 3 2 7
a[seq(2,4)]
[1] 2 7 8
See what I did
there?
Just for fun . . .
a = c(3,2,7,8)
a[a]
Just for fun . . .
a = c(3,2,7,8)
a[a]
[1] 7 2 NA NA
When would a[a] return a?
Getting at values - matrices
A = matrix(data = 0, nrow = 6, ncol = 5)
A
[,1] [,2] [,3] [,4] [,5]
[1,]
0
0
0
0
0
[2,]
0
0
0
0
0
[3,]
0
0
0
0
0
[4,]
0
0
0
0
0
[5,]
0
0
0
0
0
[6,]
0
0
0
0
0
A[3,4]
[1] 0
The order is always [row, column]
Lists
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A list is a generic holder of other variable types
Each element of a list can be anything (even another list!)
a = c(1,2,3)
b = c(10,20,30)
L = list(a,b)
L
[[1]]
[1] 1 2 3
[[2]]
[3] 10 20 30
L[[1]]
[1] 1 2 3
L[[2]][2]
[1] 20
A break to try things out
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Practicing with the function seq()
Create vectors and matrices in a few different ways
Programming in R
Functions
Loop
Programming in R
Functions
Loop
Functions
if
Output
Functions
Output
if
Output
Next topic: control elements
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for
if
while
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The general syntax is:
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for/if/while ( conditions )
{
commands
}
For
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When you want to do something a certain number of
times
When you want to do something to each element of a
vector, list, matrix . . .
X = seq(1,4,by = 1)
for(i in X)
{
print(i+1)
}
[1] 2
[1] 3
[1] 4
[1] 5
If
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When you want to execute a bit of code only if some
condition is true
X = 25
if( X < 22 )
{
print(X+1)
}
X = 20
if( X < 22 )
{
print(X+1)
}
[1] 21
<
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<=
>=
==
!=
%in% &
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If/else
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Do one thing or the other
X = 10
if( X < 22 )
{
X+1
}else(sqrt(X))
[1] 11
X = 25
if( X < 22 )
{
X+1
}else(sqrt(X))
[1] 5
<
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<=
>=
==
!=
%in% &
|
While
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Do something as long as a condition is TRUE
i = 1
while( i < 5 )
{
i = i + 1
}
i
[1] 5
<
>
<=
>=
==
!=
%in% &
|
Practice with these a bit
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For loops
While loops
Next topic: working with data
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Principles
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Read data off of hard drive
R stores it as an object (saved in your computer’s memory)
Treat that object like any other
Changes to the object are restricted to the object, they don’t
affect the data on the hard drive
Working directory
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The directory where R looks for files, or writes files
setwd() changes it
dir() shows the contents of it
setwd(“C:/Project Directory/”)
dir()
[1] “a figure.pdf”
[2] “more data.csv”
[3] “some data.csv”
Read a data file
setwd(“C:/Project Directory/”)
dir()
[1] “a figure.pdf”
[2] “more data.csv”
[3] “some data.csv”
myData = read.csv(“some data.csv”)
Writing a data file
setwd(“C:/Project Directory/”)
dir()
[1] “a figure.pdf”
[2] “more data.csv”
[3] “some data.csv”
myData = read.csv(“some data.csv”)
write.csv(myData,”updated data.csv”)
dir()
[1] “a figure.pdf”
[2] “more data.csv”
[3] “some data.csv”
[4] “updated data.csv”
Finding your way around a data frame
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head() shows the first few lines
tail() shows the last few
names() gives the column names
Pulling out columns
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Data$columnname
Data[,columnname]
Data[,3] (if columnname is the 3rd column)
Doing things to data frames
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apply!
On the board – compare for loop to apply
Practice with these
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Homework – I do not care about the answers to
questions, I care about the scripts you used to get them
Save your scripts!
Turn them in to me next week
Talk to me during the week if you have any trouble